from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI) benchmarks: perfect hyperparameters match

print_time_report()

Time report

sklearnex_KMeans_short: 0h 0m 2s

sklearnex_Ridge: 0h 0m 2s

KMeans_short: 0h 0m 3s

sklearnex_LogisticRegression: 0h 0m 6s

sklearnex_KMeans_tall: 0h 0m 11s

Ridge: 0h 0m 12s

KMeans_tall: 0h 0m 27s

LogisticRegression: 0h 0m 27s

sklearnex_KNeighborsClassifier_kd_tree: 0h 0m 32s

KNeighborsClassifier_kd_tree: 0h 2m 37s

catboost_symmetric: 0h 5m 5s

lightgbm: 0h 5m 9s

xgboost: 0h 5m 12s

HistGradientBoostingClassifier: 0h 5m 12s

catboost_lossguide: 0h 5m 18s

sklearnex_KNeighborsClassifier: 0h 6m 26s

KNeighborsClassifier: 0h 34m 34s

total: 1h 11m 42s

print_env_info()

Benchmark environment

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.8.0-1036-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.3",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.21.0",
    "scipy": "1.7.0",
    "Cython": null,
    "pandas": "1.3.0",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.156	0.0	5.131	0.0	1	1	NaN	NaN	0.568	0.0	0.274	0.0	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.140	0.0	5.699	0.0	1	100	NaN	NaN	0.565	0.0	0.248	0.0	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.145	0.0	5.532	0.0	-1	1	NaN	NaN	0.545	0.0	0.265	0.0	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.140	0.0	5.702	0.0	1	5	NaN	NaN	0.552	0.0	0.254	0.0	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.137	0.0	5.849	0.0	-1	100	NaN	NaN	0.553	0.0	0.247	0.0	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.139	0.0	5.771	0.0	-1	5	NaN	NaN	0.547	0.0	0.254	0.0	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.055	0.0	0.289	0.0	1	1	NaN	NaN	0.099	0.0	0.559	0.0	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.057	0.0	0.279	0.0	1	100	NaN	NaN	0.100	0.0	0.575	0.0	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.054	0.0	0.297	0.0	-1	1	NaN	NaN	0.106	0.0	0.509	0.0	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.061	0.0	0.261	0.0	1	5	NaN	NaN	0.101	0.0	0.608	0.0	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.061	0.0	0.262	0.0	-1	100	NaN	NaN	0.102	0.0	0.600	0.0	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.059	0.0	0.272	0.0	-1	5	NaN	NaN	0.102	0.0	0.578	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	18.329	0.260	0.0	0.018	1	1	0.705	0.948	4.665	0.075	3.929	0.084	See	See
2	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.224	0.003	0.0	0.224	1	1	1.000	1.000	0.107	0.003	2.090	0.062	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	26.739	0.146	0.0	0.027	1	100	0.925	0.948	4.649	0.040	5.751	0.059	See	See
5	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.227	0.002	0.0	0.227	1	100	1.000	1.000	0.110	0.008	2.055	0.150	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	31.936	0.000	0.0	0.032	-1	1	0.705	0.839	4.564	0.051	6.997	0.079	See	See
8	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.207	0.020	0.0	0.207	-1	1	1.000	1.000	0.108	0.002	1.910	0.186	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	26.767	0.179	0.0	0.027	1	5	0.807	0.716	4.536	0.032	5.901	0.057	See	See
11	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.233	0.006	0.0	0.233	1	5	1.000	1.000	0.107	0.002	2.165	0.066	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	40.677	0.000	0.0	0.041	-1	100	0.925	0.839	4.552	0.041	8.936	0.081	See	See
14	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.218	0.019	0.0	0.218	-1	100	1.000	1.000	0.108	0.005	2.014	0.198	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	40.966	0.000	0.0	0.041	-1	5	0.807	0.716	4.527	0.025	9.050	0.050	See	See
17	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.219	0.021	0.0	0.219	-1	5	1.000	1.000	0.108	0.002	2.018	0.198	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	12.361	0.071	0.0	0.012	1	1	0.978	0.992	1.116	0.022	11.072	0.231	See	See
20	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.017	0.001	0.0	0.017	1	1	1.000	1.000	0.005	0.000	3.249	0.313	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	20.880	0.070	0.0	0.021	1	100	0.986	0.992	1.103	0.014	18.923	0.244	See	See
23	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.023	0.001	0.0	0.023	1	100	1.000	1.000	0.005	0.000	4.706	0.406	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	25.785	0.279	0.0	0.026	-1	1	0.978	0.990	1.015	0.024	25.413	0.655	See	See
26	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.023	0.003	0.0	0.023	-1	1	1.000	1.000	0.005	0.000	4.973	0.731	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	20.840	0.138	0.0	0.021	1	5	0.986	0.984	1.015	0.012	20.538	0.279	See	See
29	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.021	0.002	0.0	0.021	1	5	1.000	1.000	0.005	0.000	4.455	0.504	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	34.004	0.000	0.0	0.034	-1	100	0.986	0.990	1.023	0.022	33.232	0.705	See	See
32	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.030	0.002	0.0	0.030	-1	100	1.000	1.000	0.005	0.001	6.083	0.855	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	34.081	0.000	0.0	0.034	-1	5	0.986	0.984	1.025	0.020	33.261	0.638	See	See
35	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.031	0.002	0.0	0.031	-1	5	1.000	1.000	0.005	0.000	6.784	0.767	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	2.736	0.0	0.029	0.0	-1	100	NaN	NaN	0.795	0.0	3.443	0.0	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	2.780	0.0	0.029	0.0	1	1	NaN	NaN	0.777	0.0	3.580	0.0	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	2.805	0.0	0.029	0.0	1	100	NaN	NaN	0.787	0.0	3.563	0.0	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	2.770	0.0	0.029	0.0	1	5	NaN	NaN	0.804	0.0	3.446	0.0	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	2.789	0.0	0.029	0.0	-1	1	NaN	NaN	0.808	0.0	3.450	0.0	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	2.713	0.0	0.029	0.0	-1	5	NaN	NaN	0.799	0.0	3.394	0.0	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.759	0.0	0.021	0.0	-1	100	NaN	NaN	0.543	0.0	1.399	0.0	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.769	0.0	0.021	0.0	1	1	NaN	NaN	0.524	0.0	1.469	0.0	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.815	0.0	0.020	0.0	1	100	NaN	NaN	0.513	0.0	1.590	0.0	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.791	0.0	0.020	0.0	1	5	NaN	NaN	0.526	0.0	1.502	0.0	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.783	0.0	0.020	0.0	-1	1	NaN	NaN	0.522	0.0	1.498	0.0	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.826	0.0	0.019	0.0	-1	5	NaN	NaN	0.518	0.0	1.595	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	2.800	0.021	0.000	0.003	-1	100	0.974	0.977	0.654	0.008	4.284	0.063	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.005	0.001	0.000	0.005	-1	100	1.000	1.000	0.002	0.001	3.490	1.422	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	0.729	0.022	0.000	0.001	1	1	0.960	0.967	0.117	0.004	6.231	0.292	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	2.529	1.482	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	4.694	0.041	0.000	0.005	1	100	0.974	0.978	0.223	0.008	21.069	0.783	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.003	0.001	0.000	0.003	1	100	1.000	1.000	0.001	0.000	5.602	2.948	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	1.463	0.064	0.000	0.001	1	5	0.970	0.978	0.230	0.012	6.359	0.431	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.002	0.001	0.000	0.002	1	5	1.000	1.000	0.001	0.000	2.388	1.403	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	0.446	0.011	0.000	0.000	-1	1	0.960	0.977	0.666	0.014	0.669	0.021	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.003	0.001	0.000	0.003	-1	1	1.000	1.000	0.002	0.001	1.892	0.746	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	0.855	0.015	0.000	0.001	-1	5	0.970	0.967	0.118	0.004	7.253	0.262	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.004	0.001	0.000	0.004	-1	5	1.000	1.000	0.000	0.000	10.362	5.813	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.050	0.002	0.000	0.000	-1	100	0.989	0.988	0.008	0.001	6.292	0.756	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.003	0.000	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	13.984	6.902	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.029	0.001	0.001	0.000	1	1	0.980	0.975	0.001	0.000	33.775	7.638	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.913	3.343	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.055	0.004	0.000	0.000	1	100	0.989	0.990	0.001	0.000	41.661	11.161	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.257	3.366	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.031	0.001	0.001	0.000	1	5	0.988	0.990	0.001	0.000	24.331	6.519	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.049	3.428	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.030	0.003	0.001	0.000	-1	1	0.980	0.988	0.008	0.001	3.681	0.699	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	14.662	8.681	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.032	0.002	0.000	0.000	-1	5	0.988	0.975	0.001	0.000	36.649	10.079	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.003	0.000	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	17.624	11.412	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.746	0.0	0.643	0.0	k-means++	NaN	30	NaN	0.373	0.0	1.999	0.0	See	See
3	KMeans_tall	fit	1000000	1000000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.603	0.0	0.795	0.0	random	NaN	30	NaN	0.319	0.0	1.889	0.0	See	See
6	KMeans_tall	fit	1000000	1000000	100	42	20.0	200	sklearn.datasets.make_blobs	30	8.777	0.0	2.734	0.0	k-means++	NaN	30	NaN	4.348	0.0	2.019	0.0	See	See
9	KMeans_tall	fit	1000000	1000000	100	42	20.0	200	sklearn.datasets.make_blobs	30	7.915	0.0	3.032	0.0	random	NaN	30	NaN	4.096	0.0	1.933	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.255	0.000	k-means++	0.001	30	0.001	0.0	0.0	8.093	3.495	See	See
2	KMeans_tall	predict	1000000	1	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.000	0.002	k-means++	1.000	30	1.000	0.0	0.0	9.413	5.164	See	See
4	KMeans_tall	predict	1000000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.250	0.000	random	0.001	30	0.001	0.0	0.0	8.278	3.202	See	See
5	KMeans_tall	predict	1000000	1	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.000	0.002	random	1.000	30	1.000	0.0	0.0	9.625	6.471	See	See
7	KMeans_tall	predict	1000000	1000	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	11.221	0.000	k-means++	0.002	30	0.001	0.0	0.0	6.693	2.828	See	See
8	KMeans_tall	predict	1000000	1	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.013	0.002	k-means++	1.000	30	1.000	0.0	0.0	9.760	5.009	See	See
10	KMeans_tall	predict	1000000	1000	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	11.606	0.000	random	0.001	30	0.002	0.0	0.0	6.077	3.170	See	See
11	KMeans_tall	predict	1000000	1	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	0.014	0.002	random	1.000	30	1.000	0.0	0.0	9.053	6.681	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.344	0.0	0.009	0.0	k-means++	NaN	20	NaN	0.058	0.0	5.979	0.0	See	See
3	KMeans_short	fit	10000	10000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.108	0.0	0.030	0.0	random	NaN	20	NaN	0.148	0.0	0.726	0.0	See	See
6	KMeans_short	fit	10000	10000	100	42	13.0	200	sklearn.datasets.make_blobs	20	1.204	0.0	0.133	0.0	k-means++	NaN	20	NaN	0.286	0.0	4.212	0.0	See	See
9	KMeans_short	fit	10000	10000	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.353	0.0	0.453	0.0	random	NaN	20	NaN	0.721	0.0	0.490	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.002	0.0	0.133	0.000	k-means++	0.001	20	0.000	0.001	0.0	3.055	0.662	See	See
2	KMeans_short	predict	10000	1	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.002	0.0	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	9.183	4.552	See	See
4	KMeans_short	predict	10000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.002	0.0	0.134	0.000	random	0.002	20	-0.001	0.001	0.0	3.144	0.750	See	See
5	KMeans_short	predict	10000	1	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.002	0.0	0.000	0.002	random	1.000	20	1.000	0.000	0.0	8.923	6.749	See	See
7	KMeans_short	predict	10000	1000	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.004	0.0	4.560	0.000	k-means++	0.216	20	0.359	0.002	0.0	2.146	0.255	See	See
8	KMeans_short	predict	10000	1	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.002	0.0	0.008	0.002	k-means++	1.000	20	1.000	0.000	0.0	9.690	4.256	See	See
10	KMeans_short	predict	10000	1000	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.004	0.0	4.523	0.000	random	0.270	20	0.319	0.002	0.0	2.080	0.290	See	See
11	KMeans_short	predict	10000	1	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.002	0.0	0.008	0.002	random	1.000	20	1.000	0.000	0.0	8.165	3.832	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_informative	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	5	5	0	42	sklearn.datasets.make_classification	[20]	17.497	0.0	[-0.06743391]	0.000	NaN	NaN	NaN	NaN	NaN	3.137	0.0	5.577	0.0	See	See
3	LogisticRegression	fit	1000	1000	10000	5	5	0	42	sklearn.datasets.make_classification	[26]	1.374	0.0	[1.51358758]	0.001	NaN	NaN	NaN	NaN	NaN	1.214	0.0	1.132	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_informative	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	5	5	0	42	sklearn.datasets.make_classification	[20]	0.000	0.000	[41.12977318]	0.0	NaN	NaN	NaN	NaN	0.533	0.000	0.0	0.862	0.488	See	See
2	LogisticRegression	predict	1000000	1	100	5	5	0	42	sklearn.datasets.make_classification	[20]	0.000	0.000	[0.17582321]	0.0	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.302	0.205	See	See
4	LogisticRegression	predict	1000	100	10000	5	5	0	42	sklearn.datasets.make_classification	[26]	0.003	0.001	[67.52632658]	0.0	NaN	NaN	NaN	NaN	0.310	0.004	0.0	0.731	0.153	See	See
5	LogisticRegression	predict	1000	1	10000	5	5	0	42	sklearn.datasets.make_classification	[26]	0.000	0.000	[13.21045073]	0.0	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.168	0.115	See	See

Ridge: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_informative	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	10	42	sklearn.datasets.make_regression	NaN	0.337	0.0	0.238	0.0	NaN	NaN	NaN	0.338	0.0	0.997	0.0	See	See
3	Ridge	fit	1000000	1000000	100	10	42	sklearn.datasets.make_regression	NaN	1.786	0.0	0.448	0.0	NaN	NaN	NaN	0.409	0.0	4.366	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_informative	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	10	42	sklearn.datasets.make_regression	NaN	0.015	0.002	5.499	0.0	NaN	NaN	0.088	0.021	0.002	0.694	0.125	See	See
2	Ridge	predict	1000	1	10000	10	42	sklearn.datasets.make_regression	NaN	0.000	0.000	0.637	0.0	NaN	NaN	NaN	0.000	0.000	0.735	0.443	See	See
4	Ridge	predict	1000000	1000	100	10	42	sklearn.datasets.make_regression	NaN	0.000	0.000	3.339	0.0	NaN	NaN	1.000	0.000	0.000	0.843	0.518	See	See
5	Ridge	predict	1000000	1	100	10	42	sklearn.datasets.make_regression	NaN	0.000	0.000	0.008	0.0	NaN	NaN	NaN	0.000	0.000	0.666	0.560	See	See